Update app.py

app.py

@@ -1,8 +1,6 @@
 import gradio as gr
 import numpy as np
 import tensorflow as tf
-import matplotlib.pyplot as plt
-import seaborn as sns
 from tensorflow.keras.models import load_model
 from tensorflow.keras.utils import to_categorical
 from sklearn.metrics import confusion_matrix
@@ -74,21 +72,35 @@ def gradio_mask(image, steps, increment):
     modified_image, original_label, predicted_label = progressively_mask_image(image, steps, increment)
     return modified_image, f"Original Label: {original_label}, New Label: {predicted_label}"
 
-# Gradio interface
-image_input = gr.Image(image_mode='L', label="Input Image")
-steps_input = gr.Slider(minimum=1, maximum=100, label="Steps", step=1, value=100)
-increment_input = gr.Slider(minimum=1, maximum=20, label="Increment", step=1, value=5)
-
-
-gr.Interface(
-    fn=gradio_mask,
-    inputs=[image_input, steps_input, increment_input],
-    outputs=[
-        gr.Image(image_mode='L', label="Ouput Image"),
-        gr.Textbox(label="Prediction Details")
-    ],
-    title="Progressive Masking",
-    description="Upload an image of a digit and observe how masking affects the model's prediction.",
-    examples=mnist_examples,
-    allow_flagging="never"
-).launch()
+def create_interface():
+    card_html = """
+    <div style="background-color: #f8f9fa; padding: 20px; border-radius: 10px; text-align: center; margin-bottom: 20px;">
+        <h1 style="font-family: Arial, sans-serif; color: #333;">Attribution Based Confidence Metric for Neural Networks</h1>
+        <h2 style="font-family: Arial, sans-serif; color: #555;">Steven Fernandes, Ph.D.</h2>
+    </div>
+    """
+
+    image_input = gr.Image(image_mode='L', label="Input Image")
+    steps_input = gr.Slider(minimum=1, maximum=100, label="Steps", step=1, value=100)
+    increment_input = gr.Slider(minimum=1, maximum=20, label="Increment", step=1, value=5)
+
+    with gr.Blocks() as demo:
+        gr.HTML(card_html)
+        gr.Interface(
+            fn=gradio_mask,
+            inputs=[image_input, steps_input, increment_input],
+            outputs=[
+                gr.Image(image_mode='L', label="Output Image"),
+                gr.Textbox(label="Prediction Details")
+            ],
+            title="Progressive Masking",
+            description="Upload an image of a digit and observe how masking affects the model's prediction.",
+            examples=mnist_examples,
+            allow_flagging="never"
+        ).launch()
+
+def main():
+    create_interface()
+
+if __name__ == "__main__":
+    main()